This study includes data on estimated latent policy diffusion networks 
from the SPID data, version 1.2. 

Compiled on June 4, 2025 by Scott Lacombe (scott-lacombe@uiowa.edu) and 
Frederick J. Boehmke (frederick-boehmke@uiowa.edu). [See note 6 below.]

For information on the SPID source data from which these are derived, see
its study page at: https://doi.org/10.7910/DVN/CVYSR7.


INCLUDED:  

1 R Markdown file: 
 
  SPID_v1.2_network.R 			R commands to generate the latent networks.
  
1 Comma separated data file: 
 
  SPID_v1.2_network.csv 		The estimated latent networks.
  
1 README file: 
  
  SPID_v1.2_network-README.txt		Describes the contents of this study.  

  
VARIABLES:

origin_statenam		string		Source state
destination_statenam	string		Recipient/emulating state
year			numeric		End of data window for that network.

  
NOTES:

1. These latent network ties are estimates based on the SPID version 
1.1 data set and cover the period 1960 to 2015. The data report the list of 
estimated latent diffusion ties. Each entry in the data indicates that 
the origin_state sent a directed policy diffusion tie to destination_statenam in 
the corresponding year. Ties are binary and directed. An alternative interpretation 
of this relationship is the destination_statenam uses the origin_state
as a policy source in that year. Directed dyads (edges) not included were estimated 
to not have a tie.

2. Estimates are generated using the NetworkInference R package developed
by Linder and Desmarais (2016), which is an R implementation of the netinf 
algorithm of Gomez Rodriguez, Leskovec, and Krause (2010). The package includes 
the SPID version 1.0 data so users do not need to download them separately.
Alternatively, users could load their own data set to generate a latent network. 

3. Over time estimates are generated by using a rolling slice (window) of the SPID 
adoption data. These estimates rely on a 100-year period, thus the estimate for 1960 
includes adoption information from 1861-1960, the estimate for 1961 includes 
adoption information from 1862-1961 and so on.

4. The NetworkInference R package requires the user to set various parameters
for the estimation. The decay parameter captures the relative contribution of recent 
adoptions compared to older adoptions. The number of edges parameter sets
the number of ties to estimate per year. We optimized these parameters using 
a grid search (see Boehmke et al. 2020). 

5. Note that Boehmke et al. 2020 list the optimal value of lambda as 4.75 in the
text of the paper. This is wrong, as Table 5 shows the optimal value is 7.75. Versions
of the network posted on this site prior to May 6, 2024 used 4.75 to estimate the 
latent network. This versions updates to use the correct value of 7.75. 

6. The previous version of the latent network only included years through 2015
instead of through 2017. This update on 06/04/2025 corrects that by including 
the estimated networks for 2016 and 2017.


RELATED WORK:

Linder, Fridolin and Bruce A. Desmarais. 2016. NetworkInference: Inferring 
latent diffusion networks. R package version 1.0. 
URL: https://github.com/desmarais-lab/NetworkInference. 

Frederick J. Boehmke; Mark Brockway; Bruce Desmarais; Jeffrey J. Harden; Scott 
LaCombe; Fridolin Linder; Hanna Wallach, 2018, "State Policy Innovation and 
Diffusion (SPID) Database v1.2", https://doi.org/10.7910/DVN/CVYSR7, 
Harvard Dataverse, V4, UNF:6:thxLqNh8In+OoHGUN4vVew== .

Boehmke, Frederick J.; Mark Brockway; Bruce A. Desmarais; Jeffrey J. Harden; 
Scott LaCombe; Fridolin Linder; and Hanna Wallach. 2020. "SPID: A New 
Database for Inferring Public Policy Innovativeness and Diffusion Networks." 
Policy Studies Journal 48(2): 517-545. doi: 10.1111/psj.12357.

Manuel Gomez Rodriguez, Jure Leskovec, and Andreas Krause. 2010. Inferring networks 
of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international 
conference on Knowledge discovery and data mining (KDD '10). ACM, New York, NY, 
USA, 1019-1028. DOI: https://doi.org/10.1145/1835804.1835933.